You should refer to Yahoo!'s terms of use (here, here, and here) for details on your rights to use the actual data downloaded. Remember - the project is intended for personal use only.
Pstock is an open source tool/project that is not affiliated in any way to yahoo-finance. Nothing in this project should be considered investment advice.
Documentation: https://obendidi.github.io/pstock
Source Code: https://github.com/obendidi/pstock
Pstock is yet another python unoficial API for getting yahoo-finance data.
The key features are:
- Async first
- Data validation using pydantic
- Fully typed, with great editor support
- Easily extensible: Parse the yahoo-finance quote dict and extract any type of info you want.
- Follows the Sans-IO design pattern: Use your favourite http library (sync/async) and let
pstockparse your response to getAssetsorBars
Python 3.8+ (support for 3.6/3.7 may be added later, contributions are welcome)
Pstock depends mainly on:
- pydantic: For data validation
- pandas: For structuring data in nice dataframes
- httpx: For the main async IO interface
$ pip install pstock-python
---> 100%- Download an asset:
import asyncio
from pstock import Asset
asset = asyncio.run(Asset.get("TSLA"))
print(asset)
# symbol='TSLA' name='Tesla, Inc.' asset_type='EQUITY' currency='USD' latest_price=920.0 sector='Consumer Cyclical' industry='Auto Manufacturers'- Download a list of assets:
import asyncio
from pstock import Assets
assets = asyncio.run(Assets.get(["TSLA", "AAPL", "GME"]))
print(assets)
# __root__=[Asset(symbol='TSLA', name='Tesla, Inc.', asset_type='EQUITY', currency='USD', latest_price=918.97, sector='Consumer Cyclical', industry='Auto Manufacturers'), Asset(symbol='AAPL', name='Apple Inc.', asset_type='EQUITY', currency='USD', latest_price=172.345, sector='Technology', industry='Consumer Electronics'), Asset(symbol='GME', name='GameStop Corp.', asset_type='EQUITY', currency='USD', latest_price=125.0, sector='Consumer Cyclical', industry='Specialty Retail')]
print(assets[0])
# Asset(symbol='TSLA', name='Tesla, Inc.', asset_type='EQUITY', currency='USD', latest_price=918.97, sector='Consumer Cyclical', industry='Auto Manufacturers')
print(assets.df)
name asset_type currency ... earnings trends income_statement
symbol ...
AAPL Apple Inc. EQUITY USD ... [{'quarter': '1Q2021', 'estimate': 0.99, 'actu... [{'date': 2021-11-17, 'strong_buy': 13, 'buy':... [{'date': 2021-09-25, 'ebit': 108949000000.0, ...
GME GameStop Corp. EQUITY USD ... [{'quarter': '1Q2021', 'estimate': 1.35, 'actu... [{'date': 2021-11-17, 'strong_buy': 2, 'buy': ... [{'date': 2021-01-30, 'ebit': -249300000.0, 't...
TSLA Tesla, Inc. EQUITY USD ... [{'quarter': '1Q2021', 'estimate': 0.79, 'actu... [{'date': 2021-11-17, 'strong_buy': 4, 'buy': ... [{'date': 2021-12-31, 'ebit': 6523000000.0, 't...- Download historical bars:
import asyncio
from pstock import Bars
bars = asyncio.run(Bars.get("TSLA"))
print(bars)
# __root__=[Bar(date=datetime.datetime(2010, 7, 1, 4, 0, tzinfo=datetime.timezone.utc), open=5.0, high=5.184000015258789, low=2.996000051498413, close=3.98799991607666, adj_close=3.98799991607666, volume=322879000.0, interval=Duration(months=1)), Bar(date=datetime.datetime(2010, 8, 1, 4, 0, tzinfo=datetime.timezone.utc), open=4.099999904632568, high=4.435999870300293, low=3.4779999256134033, close=3.8959999084472656, adj_close=3.8959999084472656, volume=75191000.0, interval=Duration(months=1)), Bar(date=datetime.datetime(2010, 9, 1, 4, 0, tzinfo=datetime.timezone.utc), open=3.9240000247955322, high=4.631999969482422, low=3.9000000953674316, close=4.081999778747559, adj_close=4.081999778747559, volume=90229500.0, interval=Duration(months=1)), Bar(date=datetime.datetime(2010, 10, 1, 4, 0, tzinfo=datetime.timezone.utc), open=4.138000011444092, high=4.374000072479248, low=4.0, close=4.368000030517578, adj_close=4.368000030517578, volume=32739000.0, interval=Duration(months=1)), ....]
print(bars.df)
open high low close adj_close volume interval
date
2010-07-01 5.000000 5.184000 2.996000 3.988000 3.988000 322879000.0 30 days
2010-08-01 4.100000 4.436000 3.478000 3.896000 3.896000 75191000.0 30 days
2010-09-01 3.924000 4.632000 3.900000 4.082000 4.082000 90229500.0 30 days
2010-10-01 4.138000 4.374000 4.000000 4.368000 4.368000 32739000.0 30 days
2010-11-01 4.388000 7.200000 4.210000 7.066000 7.066000 141575500.0 30 days
... ... ... ... ... ... ... ...
2021-11-01 1145.000000 1243.489990 978.599976 1144.760010 1144.760010 648671800.0 30 days
2021-12-01 1160.699951 1172.839966 886.119995 1056.780029 1056.780029 509945100.0 30 days
2022-01-01 1147.750000 1208.000000 792.010010 936.719971 936.719971 638471400.0 30 days
2022-02-01 935.210022 947.770020 850.700012 875.760010 875.760010 223112600.0 30 days
2022-02-15 900.000000 923.000000 893.377380 922.429993 922.429993 19085243.0 30 days
[141 rows x 7 columns]- Download stock news:
import asyncio
from pstock import News
news = asyncio.run(News.get("TSLA"))
print(news.df)
title url summary
date
2022-02-15 12:11:46+00:00 Retail investor: 'I'm being careful just in ca... https://finance.yahoo.com/news/retail-investor... Some retail investors are being more cautious ...
2022-02-15 12:23:00+00:00 Tesla’s Elon Musk Gave Away $5.7 Billion. But ... https://finance.yahoo.com/m/d342cd56-d5bb-3957... Tesla CEO Elon Musk gave away more than 5 mill...
2022-02-15 13:07:02+00:00 Company News for Feb 15, 2022 https://finance.yahoo.com/news/company-news-fe... Companies In The News Are: IFS, OLK, THS, TSLA.
....
2022-02-15 19:23:43+00:00 Australia's Syrah Resources to expand Louisian... https://finance.yahoo.com/news/australias-syra... Australian industrial materials firm Syrah Res...
2022-02-15 20:31:30+00:00 Biggest Companies in the World by Market Cap https://finance.yahoo.com/m/8aead0a5-ef35-3d90... The world's biggest companies by market cap op...An Asset in pstock terms is any ticker symbol supported by yahoo-finance. If the asset exists in yahoo-finance, you should be able to get it's quote summary using pstock.
import asyncio
from pstock import Asset
asset = asyncio.run(Asset.get("TSLA"))
print(asset)
# symbol='TSLA' name='Tesla, Inc.' asset_type='EQUITY' currency='USD' latest_price=920.0 sector='Consumer Cyclical' industry='Auto Manufacturers'An Asset will always have a:
symbol: The ticker symbol of the assetname: The long/short name of the asset (depending on which is found, the long name takes priority)asset_type: Type of the asset, can be one of:EQUITY,CURRENCY,CRYPTOCURRENCY,ETF,FUTURE,INDEXcurrency: Currency of the asset,USDfor US stockslatest_price: Latest price of the asset known by yahoo-finance, takes into account the pre-post market prices. Can benumpy.nanif no proce data is found.
Note: if an
asset_typeexists in yahoo-finance but is not one of the above, feel free to open an issue or PR. In the meantime you can subclass theAssetobject and override the type ofasset_typeand add the missing asset type
The other fields are optional and can be filled depending on the asset_type, currently there are only fields for the EQUITY (stocks) asset_type:
sectorindustry- trends
- earnings
- income_statement
In addition to getting data about a single Asset, there is also the possibily to query multiple assets at the same time using Assets. The main benefit is that it provides the ability to directly convert the resulting list of assets into a pandas dataframe.
import asyncio
from pstock import Asset
assets = asyncio.run(Assets.get(["TSLA", "AAPL", "GME"]))
print(assets.df)
name asset_type currency ... earnings trends income_statement
symbol ...
AAPL Apple Inc. EQUITY USD ... [{'quarter': '1Q2021', 'estimate': 0.99, 'actu... [{'date': 2021-11-17, 'strong_buy': 13, 'buy':... [{'date': 2021-09-25, 'ebit': 108949000000.0, ...
GME GameStop Corp. EQUITY USD ... [{'quarter': '1Q2021', 'estimate': 1.35, 'actu... [{'date': 2021-11-17, 'strong_buy': 2, 'buy': ... [{'date': 2021-01-30, 'ebit': -249300000.0, 't...
TSLA Tesla, Inc. EQUITY USD ... [{'quarter': '1Q2021', 'estimate': 0.79, 'actu... [{'date': 2021-11-17, 'strong_buy': 4, 'buy': ... [{'date': 2021-12-31, 'ebit': 6523000000.0, 't...Note 1:
Assetsis also a pydantic model that will validate data that it pulls from yahoo-finance.
Note 2: The generated pandas Dataframe is cached into a private
._dfattribute and is computed only the first time it is accessed via the property.df.
Note 3: Most if not all data objects in
pstockhave a.dfproperty, and it's the recommended way to view and manipulate data when possible.
Note 4:
Assets,Bars,Earnings,News, ... can also be iterated over and support indexing and behave like atyping.List[Asset],typing.List[Bar], ...
There are 2 ways to get the trends of a symbol.
- via
Asset:
import asyncio
from pstock import Asset
asset = asyncio.run(Asset.get("TSLA"))
print(asset.trends.df)
strong_buy buy hold sell strong_sell score recomendation
date
2021-11-17 4 4 8 6 0 2.73 HOLD
2021-12-17 11 6 13 6 0 2.39 BUY
2022-01-16 11 6 13 6 0 2.39 BUY
2022-02-15 4 4 8 6 0 2.73 HOLD- Directly via
Trends
import asyncio
from pstock import Trends
trends = asyncio.run(Trends.get("TSLA"))
print(trends.df)
strong_buy buy hold sell strong_sell score recomendation
date
2021-11-17 4 4 8 6 0 2.73 HOLD
2021-12-17 11 6 13 6 0 2.39 BUY
2022-01-16 11 6 13 6 0 2.39 BUY
2022-02-15 4 4 8 6 0 2.73 HOLDThere are 2 ways to get the earnings of a symbol.
- via
Asset:
import asyncio
from pstock import Asset
asset = asyncio.run(Asset.get("TSLA"))
print(asset.earnings.df)
estimate actual status revenue earnings
quarter
1Q2021 0.79 0.93 Beat 1.038900e+10 4.380000e+08
2Q2021 0.98 1.45 Beat 1.195800e+10 1.142000e+09
3Q2021 1.59 1.86 Beat 1.375700e+10 1.618000e+09
4Q2021 2.37 2.54 Beat 1.771900e+10 2.321000e+09
1Q2022 2.25 NaN None NaN NaN- Directly via
Earnings
import asyncio
from pstock import Earnings
earnings = asyncio.run(Earnings.get("TSLA"))
print(earnings.df)
estimate actual status revenue earnings
quarter
1Q2021 0.79 0.93 Beat 1.038900e+10 4.380000e+08
2Q2021 0.98 1.45 Beat 1.195800e+10 1.142000e+09
3Q2021 1.59 1.86 Beat 1.375700e+10 1.618000e+09
4Q2021 2.37 2.54 Beat 1.771900e+10 2.321000e+09
1Q2022 2.25 NaN None NaN NaNNote: The last earning have
NaN/Nonevalues since we only have analysts estimates and revenue isn't reported yet. The specific earnings call date can be extracted from theQuoteSummary.
There are 2 ways to get the income statement of a symbol.
Note: The current extracted statement is very limited/minimaliste, contributions are welcome to extract more data from the
QuoteSummary.
- via
Asset:
import asyncio
from pstock import Asset
asset = asyncio.run(Asset.get("TSLA"))
print(asset.income_statement.df)
ebit total_revenue gross_profit
date
2018-12-31 -2.530000e+08 2.146100e+10 4.042000e+09
2019-12-31 8.000000e+07 2.457800e+10 4.069000e+09
2020-12-31 1.951000e+09 3.153600e+10 6.630000e+09
2021-12-31 6.523000e+09 5.382300e+10 1.360600e+10Note:
asset.income_statementcan beNonefor all assets that are not of typeEQUITY.
- Directly via
IncomeStatements
import asyncio
from pstock import IncomeStatements
income_statement = asyncio.run(IncomeStatements.get("TSLA"))
print(income_statement.df)
ebit total_revenue gross_profit
date
2018-12-31 -2.530000e+08 2.146100e+10 4.042000e+09
2019-12-31 8.000000e+07 2.457800e+10 4.069000e+09
2020-12-31 1.951000e+09 3.153600e+10 6.630000e+09
2021-12-31 6.523000e+09 5.382300e+10 1.360600e+10Note: You can also use
QuarterlyIncomeStatementsfor (as the name says) quarterly income stamenets.
Gettings yahoo-finance news about a symbol also follows the same pattern.
import asyncio
from pstock import News
news = asyncio.run(News.get("TSLA"))
print(news.df)
title url summary
date
2022-02-15 12:11:46+00:00 Retail investor: 'I'm being careful just in ca... https://finance.yahoo.com/news/retail-investor... Some retail investors are being more cautious ...
2022-02-15 12:23:00+00:00 Tesla’s Elon Musk Gave Away $5.7 Billion. But ... https://finance.yahoo.com/m/d342cd56-d5bb-3957... Tesla CEO Elon Musk gave away more than 5 mill...
2022-02-15 13:07:02+00:00 Company News for Feb 15, 2022 https://finance.yahoo.com/news/company-news-fe... Companies In The News Are: IFS, OLK, THS, TSLA.
....
2022-02-15 19:23:43+00:00 Australia's Syrah Resources to expand Louisian... https://finance.yahoo.com/news/australias-syra... Australian industrial materials firm Syrah Res...
2022-02-15 20:31:30+00:00 Biggest Companies in the World by Market Cap https://finance.yahoo.com/m/8aead0a5-ef35-3d90... The world's biggest companies by market cap op...A Bar in pstock is a pydantic model with the following fields:
class Bar(BaseModel):
date: datetime
open: float
high: float
low: float
close: float
adj_close: float
volume: float
interval: timedeltaNote: The
intervalis the time between baropenandclose.
To get Bars there are a couple of arguments that can be specified:
interval: one of1m,2m,5m,15m,30m,1h,1d,5d,1mo,3mo, defaults toNoneperiod: one of1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max, defaults toNonestart: Anydate/datetimesupported by pydnatic, defaults toNoneend: Anydate/datetimesupported by pydnatic, defaults toNoneevents: one ofdiv,split,div,splits, defaults todiv,splitsinclude_prepost: Bool, include Pre and Post market bars, default toFalse
By default, if no argument is provided, the period is set to max and the interval to 3mo, example:
Note: It is possible for yahoo-finance to return bars of different interval than what was specified in the request (example below, requested
3mointerval bars, got an interval of1mobecauseTSLAis a relatively new stock and it's max period is around ~10 years by the time of writing).
import asyncio
from pstock import Bars
bars = asyncio.run(Bars.get("TSLA"))
print(bars.df)
open high low close adj_close volume interval
date
2010-07-01 5.000000 5.184000 2.996000 3.988000 3.988000 322879000.0 30 days
2010-08-01 4.100000 4.436000 3.478000 3.896000 3.896000 75191000.0 30 days
2010-09-01 3.924000 4.632000 3.900000 4.082000 4.082000 90229500.0 30 days
2010-10-01 4.138000 4.374000 4.000000 4.368000 4.368000 32739000.0 30 days
2010-11-01 4.388000 7.200000 4.210000 7.066000 7.066000 141575500.0 30 days
... ... ... ... ... ... ... ...
2021-11-01 1145.000000 1243.489990 978.599976 1144.760010 1144.760010 648671800.0 30 days
2021-12-01 1160.699951 1172.839966 886.119995 1056.780029 1056.780029 509945100.0 30 days
2022-01-01 1147.750000 1208.000000 792.010010 936.719971 936.719971 638471400.0 30 days
2022-02-01 935.210022 947.770020 850.700012 875.760010 875.760010 223112600.0 30 days
2022-02-15 900.000000 923.000000 893.377380 922.429993 922.429993 19085243.0 30 days
[141 rows x 7 columns]Note 1: Yahoo-finance limits the
intervalof data we can fetch based on how old the data is. For example we can't get1mbars for a period (or start/end) older than 7 days.
Example of an interval error ...
import asyncio
from pstock import Bars
bars = asyncio.run(Bars.get("TSLA", period="1mo", interval="1m"))
print(bars.df)
Traceback (most recent call last):
File "pstock/bar.py", line 243, in <module>
bars = asyncio.run(Bars.get("TSLA", period="1mo", interval="1m"))
File "user/.pyenv/versions/3.8.12/lib/python3.8/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "user/.pyenv/versions/3.8.12/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
return future.result()
File "pstock/bar.py", line 196, in get
return cls.load(response=response)
File "pstock/bar.py", line 169, in load
return cls.parse_obj(get_ohlc_from_chart(data))
File "user/git/pstock/pstock/utils/chart.py", line 18, in get_ohlc_from_chart
raise ValueError(f"Yahoo-finance responded with an error:\n{error}")
ValueError: Yahoo-finance responded with an error:
{'code': 'Unprocessable Entity', 'description': '1m data not available for startTime=1642289894 and endTime=1644968294. Only 7 days worth of 1m granularity data are allowed to be fetched per request.'}Note2 By leaving the
intervalparameter empty (=None),pstockautomatically tries to find the lowestintervalpossible based on how old the data requested is.
import asyncio
from pstock import Bars
bars = asyncio.run(Bars.get("TSLA", period="1mo"))
print(bars.df)
# Automatically finds that the lowest interval for a period of `1mo` is `2m`
open high low close adj_close volume interval
date
2022-01-18 14:30:00+00:00 1028.000000 1030.000000 1023.000000 1023.983582 1023.983582 1125597.0 0 days 00:02:00
2022-01-18 14:32:00+00:00 1023.230103 1032.000000 1023.230103 1029.807983 1029.807983 228889.0 0 days 00:02:00
2022-01-18 14:34:00+00:00 1029.949951 1029.949951 1023.700012 1025.000000 1025.000000 248188.0 0 days 00:02:00
2022-01-18 14:36:00+00:00 1024.319946 1025.999878 1018.000000 1021.000000 1021.000000 289773.0 0 days 00:02:00
2022-01-18 14:38:00+00:00 1021.669922 1024.000000 1018.440002 1020.150024 1020.150024 183713.0 0 days 00:02:00
... ... ... ... ... ... ... ...
2022-02-15 20:52:00+00:00 919.640015 920.989990 919.171570 919.179993 919.179993 189152.0 0 days 00:02:00
2022-02-15 20:54:00+00:00 919.320007 920.770020 918.869995 920.075012 920.075012 178398.0 0 days 00:02:00
2022-02-15 20:56:00+00:00 920.010010 921.000000 919.859985 920.940002 920.940002 207078.0 0 days 00:02:00
2022-02-15 20:58:00+00:00 920.900024 923.000000 920.750000 922.260010 922.260010 382232.0 0 days 00:02:00
2022-02-15 21:00:00+00:00 922.429993 922.429993 922.429993 922.429993 922.429993 0.0 0 days 00:02:00
[4093 rows x 7 columns]Note3 Instead of using
periodit is also possible to set a specificstartand optioallyendvalue. Ifendis not set, it defaults to current UTC time.
Sometimes we'll want to get bars for multiple symbols at the same time.
import asyncio
from pstock import BarsMulti
bars = asyncio.run(BarsMulti.get(["TSLA", "AAPL"], period="5d", interval="1d"))
print(bars.df)
TSLA AAPL
open high low close adj_close volume interval open high low close adj_close volume interval
date
2022-02-09 935.000000 946.270020 920.000000 932.000000 932.000000 17419800.0 1 days 176.050003 176.649994 174.899994 176.279999 176.279999 71285000.0 1 days
2022-02-10 908.369995 943.809998 896.700012 904.549988 904.549988 22042300.0 1 days 174.139999 175.479996 171.550003 172.119995 172.119995 90865900.0 1 days
2022-02-11 909.630005 915.960022 850.700012 860.000000 860.000000 26492700.0 1 days 172.330002 173.080002 168.039993 168.639999 168.639999 98566000.0 1 days
2022-02-14 861.570007 898.880005 853.150024 875.760010 875.760010 22515100.0 1 days 167.369995 169.580002 166.559998 168.880005 168.880005 86062800.0 1 days
2022-02-15 900.000000 923.000000 893.377380 922.429993 922.429993 19085243.0 1 days 170.970001 172.949997 170.250000 172.789993 172.789993 62512704.0 1 daysNote Bars of a specific symbol can be accessed by using the sumbol as key:
bars["TSLA"].df == bars.df["TSLA"] == Bars.get("TSLA").df
An I/O-free protocol implementation (colloquially referred to as a “sans-IO” implementation) is an implementation of a network protocol that contains no code that does any form of network I/O or any form of asynchronous flow control. Put another way, a sans-IO protocol implementation is one that is defined entirely in terms of synchronous functions returning synchronous results, and that does not block or wait for any form of I/O. ............ By keeping async flow control and I/O out of your protocol implementation, it provides the ability to use that implementation across all forms of flow control. This means that the core of the protocol implementation is divorced entirely from the way I/O is done or the way the API is designed.
-> https://sans-io.readthedocs.io
Although pstock provides an async IO interface to get data from yahoo-finance, It is still extremly easy to use it with other http libraries or other ways to get data.
A simple example is using the popular requests library:
import requests
from pstock import Asset, rdm_user_agent_value
url = Asset.uri("TSLA")
headers = {"User-Agent": rdm_user_agent_value()}
response = requests.get(url, headers=headers)
asset = Asset.load(response=response)The response object can be an str or bytes content of the response. Or it can even be the whole response object (should have a .read() method that returns content).
The same can be done for generating Bars
import requests
from pstock import Bars, rdm_user_agent_value
url = Bars.uri("TSLA", interval="1m", period="1d")
headers = {"User-Agent": rdm_user_agent_value()}
response = requests.get(url, headers=headers)
bars = Bars.load(response=response)Feel free to contribute !